infercnv results exploration for SCPCS000179

Introduction

subdiagnosis <- readr::read_tsv(
  file.path("..", "..", "..", "data", "current", params$scpca_project_id, "single_cell_metadata.tsv"),
  show_col_types = FALSE
  ) |>
  dplyr::filter(scpca_sample_id == params$sample_id) |>
  dplyr::pull(subdiagnosis)

This notebook explores using infercnv to estimate tumor and normal cells in SCPCS000179 from SCPCP000006. This sample has a(n) Anaplastic subdiagnosis.

infercnv was run using the 06_inferCNV.R script with and without a normal reference, from the same patient or from an inter-patient pull of normal cells. We tested the impact of the sub-selection of normal cells using either immune, and/or endothelial cells as healthy reference. In addition, we are exploring the use of the HMM based CNV Prediction Methods.

infercnv currently support two models for HMM-based CNV prediction, what we refer to as the i3 and i6 models. These are set in the ‘infercnv::run()’ as HMM_type=‘i3’ or HMM_type=‘i6’ (i6 is default). Each method operates on the ‘preliminary infercnv object’ which has been processed through the standard inferCNV processing routines, involving subtraction of signal corresponding to ‘normal (reference)’ cells and smoothing operations.

• i3 HMM is a three-state CNV model representing deletion, neutral, and amplification states.

• i6 HMM: a six-state CNV model that predicts the following CNV levels:

  • state 1 : 0x = complete loss
  • state 2 : 0.5x = loss of one copy
  • state 3 : 1x = neutral
  • state 4 : 1.5x = addition of one copy
  • state 5 : 2x = addition of two copies
  • state 6 : 3x = essentially a placeholder for >2x copies but modeled as 3x.

Of note, running infercnv with a i6 HMM model is very long, ~2hours of running time per sample, while i3 HMM model only requires ~10min per sample.

In this notebook, we want to compare the heatmaps of CNV profiles, and evaluate how comparable are the methods and how sensible they are to key parameters such as selection of healthy reference.

Libraries

library(infercnv)
library(SCpubr)
library(ggplot2)
library(patchwork)
library(Seurat)

Functions

Here we defined function that will be used multiple time all along the notebook.

Visualize CNV grouped by clusters or other metadata

For a Seurat object object, the function Do_CNV_heatmap load the infercnv object created with the script 06_infercnv.R using reference_value as a reference and call the function SCpubr::do_CopyNumberVariantPlot to plot the mean CNV score in each group defined in group.by.

• object is the Seurat object

• infercnv_obj is the infercnv object

• group.by is the metadata used for grouping the violin plots

• reference_value indicates the reference used for infercnv, either endothelial, immune, both or pool.

Do_CNV_heatmap <- function(object, infercnv_obj, group.by, reference_value){
  
  out <- SCpubr::do_CopyNumberVariantPlot(sample = object,
                                        infercnv_object = infercnv_obj,
                                        using_metacells = FALSE,
                                        chromosome_locations = SCpubr::human_chr_locations,
                                        return_object = FALSE,
                                        group.by = group.by
                                        )
  out <- out + 
    ggtitle(glue::glue("Copy Number Variant Plot, ", reference_value)) +
    ylab(label = "")
  
  return(out)
}

Calculate a global CNV score per cell to check the general distribution

For a Seurat object an infercnv object created with the script 06_infercnv.R using reference_value as a reference, the function Do_CNV_score calculate a CNV score per cell. The score is calculated based on the biostar discussion. The function Do_CNV_score returns the Seurat object with an additional metadata named CNV-score_{reference_value}.

• reference_value is the selection of normal cells used for infercnv
• seurat_object and infercnv_obj are the Seuratand infercnv object created with the script 06_infercnv.R

Do_CNV_score <- function(seurat_oject,infercnv_obj,reference_value){
  
  score <- apply(infercnv_obj@expr.data,2,function(x){ sum(x < 0.95 | x > 1.05)/length(x) })
  
  seurat_obj <- AddMetaData(seurat_oject, score, col.name = glue::glue("CNV-score_", reference_value))
  return(seurat_obj)
}

Visualize seurat clusters and metadata

For a Seurat object objectand a metadata metadata, the function visualize_metadata will plot FeaturePlot and BarPlot

• object is the Seurat object

• meta the gene or quantitative value to be plotted

• group.by is the metadata used for grouping the violin plots

visualize_metadata <- function(object, meta, group.by){
  
  if(is.numeric(object@meta.data[,meta])){
     d <- SCpubr::do_FeaturePlot(object, 
                                              features = meta, 
                                              pt.size = 0.2, 
                                              legend.width = 0.5, 
                                              legend.length = 5, 
                                              legend.position = "right") + ggtitle(meta)
    b <- SCpubr::do_ViolinPlot(object, 
                                             features = meta, 
                                             ncol = 1, 
                                             group.by = group.by, 
                                             legend.position = "none")
                   
   return(d + b + plot_layout(ncol = 2, widths = c(2,4))) 
  }
  
  
  else{
    
  
  d <- SCpubr::do_DimPlot(object, reduction="umap", group.by = group.by, label = TRUE, repel = TRUE) + ggtitle(paste0(meta," - umap")) + theme(text=element_text(size=18))
  b <- SCpubr::do_BarPlot(sample = object,
                         group.by = meta,
                         split.by = group.by,
                         position = "fill",
                         font.size = 10,
                         legend.ncol = 3) +
                         ggtitle("% cells")+
                         xlab(print(group.by)) +
                         theme(text=element_text(size=18))
  return(d + b + plot_layout(ncol = 2, widths = c(2,4)))
  }
  
}

Visualize seurat clusters and markers genes

For a Seurat object objectand a features features, the function visualize_feature will plot FeaturePlot and ViolinPlot

• object is the Seurat object

• feature the gene or quantitative value to be plotted

• group.by is the metadata used for grouping the violin plots

visualize_feature <- function(object, feature, group.by ){
 
                  d <- SCpubr::do_FeaturePlot(object, 
                                              features = feature, 
                                              pt.size = 0.2, 
                                              legend.width = 0.5, 
                                              legend.length = 5, 
                                              legend.position = "right") + ggtitle(as.character(feature))
                  b <- SCpubr::do_ViolinPlot(object, 
                                             features = feature, 
                                             ncol = 1, 
                                             group.by = group.by, 
                                             legend.position = "none",
                                             assay = "SCT") + ylab(as.character(feature))
                   
                  return(d + b + plot_layout(ncol = 2, widths = c(2,4))) 
}

Visualize CNV density

For a Seurat object objectand a features features, the function visualize_feature will plot FeaturePlot and ViolinPlot

• object is the Seurat object

• features the gene or quantitative value to be plotted

• group.by is the metadata used for grouping the violin plots

visualize_density <- function(object, features, group.by ){
 
                  d <- SCpubr::do_RidgePlot(object, 
                                            feature = features, 
                                            group.by = group.by, 
                                             legend.position = "none",
                                             assay = "SCT") + ylab(features)
                   
                  return(d) 
}

Wrapper function to explore infercnv HMM cnv prediction

The wrapper_explore_hmm take as input the infercnv_obj generated with infercnv HMM cnv predictions. The wrapper allows the following steps and plots:

Feature plot and repartition of the CNV per chromosome

For each chromosome, we look at the repartition of the proportion_cnv_ in cells labeled as immune, endothelial, stroma and fetal nephron. proportion_cnv_ is the proportion in number of genes that are part of any cnv/loss/duplication in the given chr.

Distribution of CNV estimation in the Wilms tumor copartments

For each chromosome, we look at the distribution of the proportion_cnv_ in cells labeled as immune, endothelial, stroma and fetal nephron. proportion_cnv_ is the proportion in number of genes that are part of any cnv/loss/duplication in the given chr.

We are quite confident that immune and endothelial cells are well identified by label transfer done in 02b_label-transfer_fetal_kidney_reference_Stewart.Rmd. The distribution of CNV for endothelial and immune cells should thus be a single peak center on 0.

We do not know if fetal nephron and stroma cells are a mix of normal and cancer cells. Would they be a group of normal cells, we should expect a single peak center on 0 for every chromosome. As we expect to have a large number of cancer with heterogeneous CNV, we should see multiple peaks.

DotPlot

The Dotplot representation summarizes the percentage of cells in each compartment with cnv in each of the 22 chromosomes.

CNV score

Finally, we try to calculate single CNV score and assess its potential in identifying cells with CNV versus normal cells without CNV.

We simply checked for each chromosome if the cell has_cnv_chr. Would the cell have more than cnv_threshold chromosome with CNV, the global has_cnv_score will be TRUE. Else, the cell will have a has_cnv_score set to FALSE.

wrapper_explore_hmm <- function(infercnv_obj, cnv_threshold = 1){

  p <- list()

  #### Plot UMAP of known Wilms tumor related alteration
  p[["compartment"]] <- visualize_metadata(infercnv_obj, "fetal_kidney_predicted.compartment", "seurat_clusters")
  
  #### Feature plot and repartition of the CNV per chromosome
  for(i in 1:22){
    tmp1 <- visualize_feature(infercnv_obj, feature=glue::glue("proportion_cnv_chr", i), group.by = "fetal_kidney_predicted.compartment")
    tmp2 <- visualize_density(infercnv_obj, features=glue::glue("proportion_cnv_chr", i), group.by = "fetal_kidney_predicted.compartment")
    p[[glue::glue("proportion_cnv_chr", i)]] <- tmp1 + tmp2 + plot_layout(ncol = 3, widths = c(1,2,2))
  }
  
  #### DotPlot
  p[["Dotplot"]] <- DotPlot(infercnv_obj, features =  colnames(infercnv_obj@meta.data)[grepl("has_cnv_chr", colnames(infercnv_obj@meta.data))], group.by = "fetal_kidney_predicted.compartment", assay = "SCT") + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  
  #### CNV score
  meta <- infercnv_obj@meta.data
  meta$has_cnv_score <- rowSums(meta[,grepl("has_cnv_chr", colnames(meta))])
  meta$has_cnv_score[meta$has_cnv_score > cnv_threshold] <- "CNV"

  # we know that some immune cells might have false positive CNV 
  meta$has_cnv_score[meta$has_cnv_score %in% c(0:cnv_threshold)] <- "no CNV"
  infercnv_obj <- AddMetaData(infercnv_obj, meta$has_cnv_score, col.name =  "has_cnv_score") 
  p[["infercnv_obj"]] <- infercnv_obj

  p[["cnv_seurat"]] <- visualize_metadata(infercnv_obj, "has_cnv_score", "seurat_clusters")
  p[["cnv_compartment"]] <- visualize_metadata(infercnv_obj, "has_cnv_score", "fetal_kidney_predicted.compartment")

return(p)

  
}

Base directories

# The base path for the OpenScPCA repository, found by its (hidden) .git directory
repository_base <- rprojroot::find_root(rprojroot::is_git_root)

# The path to this module
module_base <- file.path(repository_base, "analyses", "cell-type-wilms-tumor-06")

Input files

The input for this notebook are the results of 06_inferCNV.R

result_dir <- file.path(module_base, "results", params$sample_id, "06_infercnv")

Seurat object

We load the Seurat object generated in 06_infercnv.R

srat <- readRDS(file.path(module_base, "results", params$sample_id, glue::glue("02b-fetal_kidney_label-transfer_", params$sample_id, ".Rds")))

Analysis

Heatmap of infercnv results

Here we plot the output of infercnv as heatmaps of CNV. We first look at the png file generated by the infercnv function. We then used the infercnv object to look at mean CNV value across compartments (immune, endothelial, stroma and fetal nephron).

Without reference

With immune cells as reference

With endothelium cells as reference

With immune and endothelium cells as reference

With immune and endothelium cells from all upfront resection samples spiked-in as reference

Summarize CNV per chromosome and compartment

infercnv_obj <- list()
for(reference_value in c("reference-none", "reference-immune", "reference-endothelium", "reference-both")){
  infercnv_obj[[reference_value]] <- readRDS(file.path(result_dir, glue::glue(reference_value, "_HMM-no") , glue::glue("06_infercnv_", params$sample_id, "_", reference_value, "_HMM-no.rds")))
  print(Do_CNV_heatmap(object = srat, infercnv_obj = infercnv_obj[[reference_value]], group.by = "fetal_kidney_predicted.compartment", reference_value = reference_value))
}

These heatmaps emphasize the importance of the selection of normal cells prior the inference of CNV. The normal reference should contain as much cell types as possible, in order to minimize false positive CNV. In our case, we should take immune and entodethial cells when possible.

Of note: By default if no reference is provided, infercnv take the mean of expression as normal reference. The risk is that the main cell population (in our case the fetal nephron compartment) might be mistaken as the normal baseline.

Summary CNV score

We want to calculate a single CNV score and asess if/how it can be use to define cells with CNV versus stable/normal cells. We defined the score as discribed in the biostar discussion.

We would expect:

• immune and endothelial cells to have a low CNV score

• fetal nephron and stroma cells to have low to high CNV score.

Ideally, the CNV score distribution should show two peaks, one for the normal cells with no CNV, one for cancer cells with more genomic rearrangements.

for(reference_value in c("reference-none", "reference-immune", "reference-endothelium", "reference-both")){
  srat <- Do_CNV_score(srat,infercnv_obj = infercnv_obj[[reference_value]], reference_value)
  p1 <- visualize_feature(srat, feature =  glue::glue("CNV-score_", reference_value) , group.by = "fetal_kidney_predicted.compartment" )
  p2 <- visualize_density(srat, features=glue::glue("CNV-score_", reference_value), group.by = "fetal_kidney_predicted.compartment")
  print(p1 + p2 + plot_layout(ncol = 3, widths = c(1,2,2)))
}

This unique CNV score does not look promising. We will not use this score in future notebooks.

We might have to select chromosomes we would like to look at, i.e. the one relevant for Wilms tumor (1p, 1q, chr17).

HMM-i3 inference prediction with both immune and endothelium cells as reference

We then explore infercnv results generated with immune and endothelial cells as reference, using a HMM-i3 prediction models.

We load the Seurat object generated in 06_infercnv.R

#### Load the `Seurat` object
srat_i3 <- readRDS(file.path(module_base, "results", params$sample_id, glue::glue("06_infercnv_HMM-i3_", params$sample_id, "_reference-both.rds")))

and explore the CNV results using the wrapper_explore_hmm fucntion.

p <- list()
p <- wrapper_explore_hmm(srat_i3, cnv_threshold = 1)

## [1] "seurat_clusters"
## [1] "seurat_clusters"
## [1] "fetal_kidney_predicted.compartment"

p

## $compartment

## 
## $proportion_cnv_chr1

## 
## $proportion_cnv_chr2

## 
## $proportion_cnv_chr3

## 
## $proportion_cnv_chr4

## 
## $proportion_cnv_chr5

## 
## $proportion_cnv_chr6

## 
## $proportion_cnv_chr7

## 
## $proportion_cnv_chr8

## 
## $proportion_cnv_chr9

## 
## $proportion_cnv_chr10

## 
## $proportion_cnv_chr11

## 
## $proportion_cnv_chr12

## 
## $proportion_cnv_chr13

## 
## $proportion_cnv_chr14

## 
## $proportion_cnv_chr15

## 
## $proportion_cnv_chr16

## 
## $proportion_cnv_chr17

## 
## $proportion_cnv_chr18

## 
## $proportion_cnv_chr19

## 
## $proportion_cnv_chr20

## 
## $proportion_cnv_chr21

## 
## $proportion_cnv_chr22

## 
## $Dotplot

## 
## $infercnv_obj
## An object of class Seurat 
## 88749 features across 6358 samples within 2 assays 
## Active assay: RNA (60319 features, 0 variable features)
##  1 layer present: counts
##  1 other assay present: SCT
##  2 dimensional reductions calculated: pca, umap
## 
## $cnv_seurat

## 
## $cnv_compartment

HMM-i6 prediction of CNV with intra-patient normal reference (immune and endothelial)

We then explore infercnv results generated with immune and endothelial cells as reference, using a HMM-i6 model.

We load the Seurat object generated in 06_infercnv.R

srat_i6 <- readRDS(file.path(module_base, "results", params$sample_id, glue::glue("06_infercnv_HMM-i6_", params$sample_id, "_reference-both.rds")))

q <- list()
q <- wrapper_explore_hmm(srat_i6, cnv_threshold = 1)

## [1] "seurat_clusters"
## [1] "seurat_clusters"
## [1] "fetal_kidney_predicted.compartment"

q

## $compartment

## 
## $proportion_cnv_chr1

## 
## $proportion_cnv_chr2

## 
## $proportion_cnv_chr3

## 
## $proportion_cnv_chr4

## 
## $proportion_cnv_chr5

## 
## $proportion_cnv_chr6

## 
## $proportion_cnv_chr7

## 
## $proportion_cnv_chr8

## 
## $proportion_cnv_chr9

## 
## $proportion_cnv_chr10

## 
## $proportion_cnv_chr11

## 
## $proportion_cnv_chr12

## 
## $proportion_cnv_chr13

## 
## $proportion_cnv_chr14

## 
## $proportion_cnv_chr15

## 
## $proportion_cnv_chr16

## 
## $proportion_cnv_chr17

## 
## $proportion_cnv_chr18

## 
## $proportion_cnv_chr19

## 
## $proportion_cnv_chr20

## 
## $proportion_cnv_chr21

## 
## $proportion_cnv_chr22

## 
## $Dotplot

## 
## $infercnv_obj
## An object of class Seurat 
## 88749 features across 6358 samples within 2 assays 
## Active assay: RNA (60319 features, 0 variable features)
##  1 layer present: counts
##  1 other assay present: SCT
##  2 dimensional reductions calculated: pca, umap
## 
## $cnv_seurat

## 
## $cnv_compartment

HMM-i3 CNV prediction model with an inter-patient reference of immune cells (endothelial and immune)

We lastly explore infercnv results generated with a pull of immune and endothelial cells from different Wilms tumor samples as reference, using a HMM-i3 prediction model.

The selection of normal cells to be spiked-in as a normal reference prior running infercnv has been performed in `06b_build-normal-cell_reference.R. We selected samples that haven’t been pre-treated with chemotherapies. Indeed, even if normal cells shouldn’t be affected by chemotherapy in terms of CNV, we are not sure how chemotherapies can affect the CNV profile of normal cells. We thus decided to only take endothelial and immune cells from non-treated samples whose label transfer scores are greater than 0.85 to build the reference of normal cells.

Load the Seurat object

We load the Seurat object generated in 06_infercnv.R and transfer the infercnv metadata to the main Seurat object of sample SCPCS000179 saved in 02b-fetal_kidney_label-transfer_SCPCS000179.Rds.

Such a transfer is required as the merged Seurat object + spike-in cells does not contained the umap reduction.

seurat_full <- readRDS(file.path(module_base, "results", params$sample_id, glue::glue("06_infercnv_HMM-i3_", params$sample_id, "_reference-pull.rds")))
# subset non spike-in cells
keep_cells <- colnames(srat)
seurat <- subset(seurat_full, cells = keep_cells)

meta <- seurat@meta.data[colnames(srat),]

# merge the new info into the srat metadata
srat_pull <- AddMetaData(srat, meta[,grepl("_cnv_chr", colnames(meta))], col.name = colnames(meta[grepl("_cnv_chr", colnames(meta))]))

pull <- list()
pull <- wrapper_explore_hmm(srat_pull, cnv_threshold = 3)

## [1] "seurat_clusters"
## [1] "seurat_clusters"
## [1] "fetal_kidney_predicted.compartment"

pull

## $compartment

## 
## $proportion_cnv_chr1

## 
## $proportion_cnv_chr2

## 
## $proportion_cnv_chr3

## 
## $proportion_cnv_chr4

## 
## $proportion_cnv_chr5

## 
## $proportion_cnv_chr6

## 
## $proportion_cnv_chr7

## 
## $proportion_cnv_chr8

## 
## $proportion_cnv_chr9

## 
## $proportion_cnv_chr10

## 
## $proportion_cnv_chr11

## 
## $proportion_cnv_chr12

## 
## $proportion_cnv_chr13

## 
## $proportion_cnv_chr14

## 
## $proportion_cnv_chr15

## 
## $proportion_cnv_chr16

## 
## $proportion_cnv_chr17

## 
## $proportion_cnv_chr18

## 
## $proportion_cnv_chr19

## 
## $proportion_cnv_chr20

## 
## $proportion_cnv_chr21

## 
## $proportion_cnv_chr22

## 
## $Dotplot

## 
## $infercnv_obj
## An object of class Seurat 
## 88749 features across 6358 samples within 2 assays 
## Active assay: RNA (60319 features, 0 variable features)
##  1 layer present: counts
##  1 other assay present: SCT
##  2 dimensional reductions calculated: pca, umap
## 
## $cnv_seurat

## 
## $cnv_compartment

Comparisons of inter- and intra-patient global cnv score with HMM prediction model

We compare here the binary CNV scores calculated with the three HMM prediction models:

• HMM-i3 with inter-patient endothelial and immunce cells as reference

• HMM-i3 with intra-patient endothelial and immunce cells as reference

• HMM-i6 with intra-patient endothelial and immunce cells as reference

p1 <- SCpubr::do_DimPlot(pull$infercnv_obj, "has_cnv_score", plot.title = "inter-patient reference, HMM-i3", reduction = "umap")

p2 <- SCpubr::do_DimPlot(p$infercnv_obj, "has_cnv_score", plot.title = "intra-patient reference, HMM-i3", reduction = "umap")

p3 <- SCpubr::do_DimPlot(q$infercnv_obj, "has_cnv_score", plot.title = "intra-patient reference, HMM-i6", reduction = "umap")

p1 + p2 + p3 + plot_layout(ncol = 3)

Conclusions

• We should run infercnv with the most diverse normal reference as possible, including different cell types. Providing no reference is not a good option, as we think that most of the cells are cancer cells with few CNV. In our case, we advise taking at least immune and endothelial cells as normal reference.

• The HMM prediction models help exploring the infercnv results. In this notebook, we have compared three HMM prediction models:

  • HMM-i3 with inter-patient endothelial and immunce cells as reference

  • HMM-i3 with intra-patient endothelial and immunce cells as reference

  • HMM-i6 with intra-patient endothelial and immunce cells as reference

• Globally, the three scores seems to drive similar conclusions, with the majority of fetal nephron and stroma cells being cancer cells, at least in the sample selected.

  • The HMM-i3 model with inter-patient endothelial and immunce cells as reference has the advantage to be usable for all Wilms tumor samples, including the ones with a very low number of immune and/or endothelial cells.

  • The HMM-i3 model with intra-patient endothelial and immunce cells as reference seems to be the cleaner, ~fast to run (10 minutes per samples) and is more precise than the HMM-i3 with the inter-patient reference.

  • The HMM-i6 model with intra-patient endothelial and immunce cells as reference is very slow (~2 hours per sample) and couldn’t be used for the entire cohorte. It is more noisy than the i3 version. However, it could have the potential to detect cancer cells with very low CNV profile.

• Surprisingly, running infercnv with emdothelial and immune cells from (i) the same patient or (ii) a set of Wilms tumor patients do not seem to affect drastically the results. Some false positive CNV might occur in every patient due to the inter-patient variability. By comparing the results in conditions (i) and (ii), we should be able to understand which false positive are recurrent and do not take them into account.

Session Info

# record the versions of the packages used in this analysis and other environment information
sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
##  [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] Seurat_5.1.0       SeuratObject_5.0.2 sp_2.1-4           patchwork_1.2.0    ggplot2_3.5.1      SCpubr_2.0.2       infercnv_1.20.0   
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.22            splines_4.4.1               later_1.3.2                 ggplotify_0.1.2             bitops_1.0-8               
##   [6] tibble_3.2.1                polyclip_1.10-7             fastDummies_1.7.4           lifecycle_1.0.4             fastcluster_1.2.6          
##  [11] edgeR_4.2.1                 doParallel_1.0.17           rprojroot_2.0.4             globals_0.16.3              lattice_0.22-6             
##  [16] vroom_1.6.5                 MASS_7.3-61                 ggdist_3.3.2                magrittr_2.0.3              limma_3.60.4               
##  [21] plotly_4.10.4               sass_0.4.9                  rmarkdown_2.28              jquerylib_0.1.4             yaml_2.3.10                
##  [26] httpuv_1.6.15               sctransform_0.4.1           spam_2.10-0                 spatstat.sparse_3.1-0       reticulate_1.38.0          
##  [31] cowplot_1.1.3               pbapply_1.7-2               RColorBrewer_1.1-3          multcomp_1.4-26             abind_1.4-5                
##  [36] zlibbioc_1.50.0             Rtsne_0.17                  GenomicRanges_1.56.1        purrr_1.0.2                 BiocGenerics_0.50.0        
##  [41] yulab.utils_0.1.7           TH.data_1.1-2               sandwich_3.1-1              GenomeInfoDbData_1.2.12     IRanges_2.38.1             
##  [46] S4Vectors_0.42.1            ggrepel_0.9.5               irlba_2.3.5.1               listenv_0.9.1               spatstat.utils_3.1-0       
##  [51] goftest_1.2-3               RSpectra_0.16-2             spatstat.random_3.3-1       fitdistrplus_1.2-1          parallelly_1.38.0          
##  [56] coin_1.4-3                  leiden_0.4.3.1              codetools_0.2-20            DelayedArray_0.30.1         tidyselect_1.2.1           
##  [61] futile.logger_1.4.3         farver_2.1.2                UCSC.utils_1.0.0            viridis_0.6.5               rjags_4-16                 
##  [66] matrixStats_1.3.0           stats4_4.4.1                spatstat.explore_3.3-2      jsonlite_1.8.8              progressr_0.14.0           
##  [71] ggridges_0.5.6              survival_3.7-0              iterators_1.0.14            foreach_1.5.2               tools_4.4.1                
##  [76] ica_1.0-3                   Rcpp_1.0.13                 glue_1.7.0                  gridExtra_2.3               SparseArray_1.4.8          
##  [81] xfun_0.47                   distributional_0.5.0        MatrixGenerics_1.16.0       GenomeInfoDb_1.40.1         dplyr_1.1.4                
##  [86] withr_3.0.1                 formatR_1.14                fastmap_1.2.0               fansi_1.0.6                 caTools_1.18.2             
##  [91] digest_0.6.37               gridGraphics_0.5-1          parallelDist_0.2.6          R6_2.5.1                    mime_0.12                  
##  [96] colorspace_2.1-1            scattermore_1.2             gtools_3.9.5                tensor_1.5                  spatstat.data_3.1-2        
## [101] utf8_1.2.4                  tidyr_1.3.1                 generics_0.1.3              data.table_1.16.0           httr_1.4.7                 
## [106] htmlwidgets_1.6.4           S4Arrays_1.4.1              uwot_0.2.2                  pkgconfig_2.0.3             gtable_0.3.5               
## [111] modeltools_0.2-23           lmtest_0.9-40               SingleCellExperiment_1.26.0 XVector_0.44.0              htmltools_0.5.8.1          
## [116] dotCall64_1.1-1             scales_1.3.0                Biobase_2.64.0              png_0.1-8                   phyclust_0.1-34            
## [121] spatstat.univar_3.0-0       knitr_1.48                  lambda.r_1.2.4              rstudioapi_0.16.0           tzdb_0.4.0                 
## [126] reshape2_1.4.4              coda_0.19-4.1               nlme_3.1-166                cachem_1.1.0                zoo_1.8-12                 
## [131] stringr_1.5.1               KernSmooth_2.23-24          libcoin_1.0-10              parallel_4.4.1              miniUI_0.1.1.1             
## [136] pillar_1.9.0                grid_4.4.1                  vctrs_0.6.5                 gplots_3.1.3.1              RANN_2.6.2                 
## [141] promises_1.3.0              xtable_1.8-4                cluster_2.1.6               evaluate_0.24.0             readr_2.1.5                
## [146] locfit_1.5-9.10             mvtnorm_1.3-1               cli_3.6.3                   compiler_4.4.1              futile.options_1.0.1       
## [151] rlang_1.1.4                 crayon_1.5.3                future.apply_1.11.2         labeling_0.4.3              forcats_1.0.0              
## [156] argparse_2.2.3              plyr_1.8.9                  fs_1.6.4                    stringi_1.8.4               viridisLite_0.4.2          
## [161] deldir_2.0-4                assertthat_0.2.1            munsell_0.5.1               lazyeval_0.2.2              spatstat.geom_3.3-2        
## [166] Matrix_1.7-0                RcppHNSW_0.6.0              hms_1.1.3                   bit64_4.0.5                 future_1.34.0              
## [171] statmod_1.5.0               shiny_1.9.1                 highr_0.11                  SummarizedExperiment_1.34.0 ROCR_1.0-11                
## [176] igraph_2.0.3                RcppParallel_5.1.9          bslib_0.8.0                 bit_4.0.5                   ape_5.8